def select_caida_backbone():
ROOT = '/home/wangjing/LocalResearch/CyberData/CaidaData/'
T = 4.33
dur_set = np.linspace(0.1, T*0.9, 20)
tr = dict(alphav=[], lkav=[], betav=[], lkbv=[], dur=[])
for dur in dur_set:
print('dur', dur)
f_name = ROOT + 'passive-2013-sigs-%f/sigs.pk' % (dur)
sigs = load(f_name)
s_v = mg_sample(n=min([4, len(sigs['sig_edges'])]), k=200, **sigs)
alpha, lka = mle(s_v, 'BA')
beta, lkb = mle(s_v, 'ER')
tr['dur'].append(dur)
tr['alphav'].append(alpha)
tr['betav'].append(beta)
tr['lkav'].append(lka)
tr['lkbv'].append(lkb)
dump(tr, './model-select-caida-backbone.pk')
def select_caida_backbone():
ROOT = '/home/wangjing/LocalResearch/CyberData/CaidaData/'
T = 4.33
dur_set = np.linspace(0.1, T * 0.9, 20)
tr = dict(alphav=[], lkav=[], betav=[], lkbv=[], dur=[])
for dur in dur_set:
print('dur', dur)
f_name = ROOT + 'passive-2013-sigs-%f/sigs.pk' % (dur)
sigs = load(f_name)
s_v = mg_sample(n=min([4, len(sigs['sig_edges'])]), k=200, **sigs)
alpha, lka = mle(s_v, 'BA')
beta, lkb = mle(s_v, 'ER')
tr['dur'].append(dur)
tr['alphav'].append(alpha)
tr['betav'].append(beta)
tr['lkav'].append(lka)
tr['lkbv'].append(lkb)
dump(tr, './model-select-caida-backbone.pk')
def select_simple_pkt():
ROOT = '/home/wangjing/LocalResearch/CyberData/CaidaData/'
T = 66095.977196
# msv = []
dur_set = np.linspace(10, T*0.9, 50)
tr = dict(alphav=[], lkav=[], betav=[], lkbv=[], dur=[])
for dur in dur_set:
print('dur', dur)
f_name = ROOT+'sigs1/loc6-%i/sigs.pk' % (dur)
sigs = load(f_name)
s_v = mg_sample(n=min([4, len(sigs['sig_edges'])]), k=400, **sigs)
alpha, lka = mle(s_v, 'BA')
beta, lkb = mle(s_v, 'ER')
tr['dur'].append(dur)
tr['alphav'].append(alpha)
tr['betav'].append(beta)
tr['lkav'].append(lka)
tr['lkbv'].append(lkb)
dump(tr, './model-select-simple-pkt.pk')
def select_simple_pkt():
ROOT = '/home/wangjing/LocalResearch/CyberData/CaidaData/'
T = 66095.977196
# msv = []
dur_set = np.linspace(10, T * 0.9, 50)
tr = dict(alphav=[], lkav=[], betav=[], lkbv=[], dur=[])
for dur in dur_set:
print('dur', dur)
f_name = ROOT + 'sigs1/loc6-%i/sigs.pk' % (dur)
sigs = load(f_name)
s_v = mg_sample(n=min([4, len(sigs['sig_edges'])]), k=400, **sigs)
alpha, lka = mle(s_v, 'BA')
beta, lkb = mle(s_v, 'ER')
tr['dur'].append(dur)
tr['alphav'].append(alpha)
tr['betav'].append(beta)
tr['lkav'].append(lka)
tr['lkbv'].append(lkb)
dump(tr, './model-select-simple-pkt.pk')
from SBDet import *
import pylab as P
import networkx as nx
from subprocess import check_call
from SBDet.Util import load, zload
def get_ips(data, format_=None):
nnx = NetworkXGraph(data=data)
if format_ is not None:
return [format_(ip) for ip in nnx.get_vertices()]
return nnx.get_vertices()
ab_ids = range(200, 230)
sigs = load('./Result/sigs_nx.pk')
adj_mats = [nx.to_scipy_sparse_matrix(sigs[i]) for i in ab_ids]
#### Identify the Pivot Nodes ######
tr = load('./Result/GCM_tr.pk')
weights = tr['solution']
p_nodes = ident_pivot_nodes(adj_mats, weights, 0.8)
#### Calculate interactions of nodes with pivot nodes ####
inta = cal_inta_pnodes(adj_mats, tr['solution'], p_nodes)
#### Calculate the correlation graph ####
A, npcor = cal_cor_graph(adj_mats, p_nodes, 0.2)
w2 = 0.01
P0, q0, W = com_det_reg(A, inta, w1=0, w2=w2, lamb=0, out='./prob.sdpb')
#!/usr/bin/env python
from __future__ import print_function, division
from SBDet import *
import pylab as P
import networkx as nx
from subprocess import check_call
from SBDet.Util import load, zload
def get_ips(data, format_=None):
nnx = NetworkXGraph(data=data)
if format_ is not None:
return [format_(ip) for ip in nnx.get_vertices()]
return nnx.get_vertices()
ab_ids = range(200, 230)
sigs = load('./Result/sigs_nx.pk')
adj_mats = [nx.to_scipy_sparse_matrix(sigs[i]) for i in ab_ids]
#### Identify the Pivot Nodes ######
tr = load('./Result/GCM_tr.pk')
weights = tr['solution']
p_nodes = ident_pivot_nodes(adj_mats, weights, 0.8)
#### Calculate interactions of nodes with pivot nodes ####
inta = cal_inta_pnodes(adj_mats, tr['solution'], p_nodes)
#### Calculate the correlation graph ####
A, npcor = cal_cor_graph(adj_mats, p_nodes, 0.2)
w2 = 0.01
P0, q0, W = com_det_reg(A, inta, w1=0, w2=w2, lamb=0, out='./prob.sdpb')
